A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier

By Levent Tolga Eren, Turker Ince, Serkan Kiranyaz. Journal of Signal Processing Systems, 2018

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Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, positive): “[112], a 1D DCNN-based method for bearing fault diagnosis has been developed that takes advantage of the shift-invariance of the convolution operation in order to eliminate the need for time-domain signal alignment.”
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis (2019, methodology): “For example, Eren [23] employed compact adaptive 1D-CNN classifier for a generic real-time induction bearing fault diagnosis system.”
Fault Description Based Attribute Transfer for Zero-Sample Industrial Fault Diagnosis (2021, methodology): “The feature extraction is usually done by matrix decomposition methods [6], time-frequency analysis [7], and the recent convolutional neural networks [8], [9] to realize the dimensionality reduction and denosing.”
1D Convolutional Neural Networks and Applications: A Survey (2019, result): “Similar to other applications, 1D CNNs have the unique capability to optimize both feature extraction and classification in a single learning body and naturally, the three recent studies [53,56,57], have shown that a real-time monitoring and instantaneous anomaly detection can be accomplished with a”